Machine model estimating device of electric motor control apparatus

ABSTRACT

A machine model estimating device of an electric motor control apparatus having an electric motor for driving a load machine, a rotation detector for detecting a rotating angle of the electric motor, and a servo control device for controlling the electric motor. The machine has a calculating device for outputting an operation command signal for operating the electric motor to the servo control device, and frequency characteristic equations for a rigid body model and an N-inertia model, N being an integer which is equal to or greater than 2, which are previously input to the calculating device. The calculating device includes a frequency characteristic measuring section, a frequency characteristic peak detecting section, an attenuation estimation value analyzing section, a frequency characteristic error calculating section, and a machine model deciding section.

TECHNICAL FIELD

The present invention relates to a machine model estimating device of anelectric motor control apparatus which can faithfully estimate a machinemodel to be easily utilized for a simulation and a servo regulation byautomatically reading an anti-resonance frequency, a resonance frequencyand an attenuation from a frequency characteristic measured valuewithout using an expensive measuring apparatus even if an operator hasneither advanced expertise nor experiences, and is inexpensive.

BACKGROUND ART

Conventionally, an electric motor control apparatus to be used in asemiconductor manufacturing apparatus, a positioning apparatus such as amachine tool or an industrial robot is constituted as shown in FIG. 15.

FIG. 15 is a view showing the whole structure of the electric motorcontrol apparatus according to the conventional art, and descriptionwill be given by taking a positioning apparatus as an example.

In the drawing, 2 denotes a servo control device, 3 denotes a rotationdetector, 4 denotes an electric motor, 5 denotes a transmittingmechanism, 6 denotes a movable section, and 7 denotes a non-movablesection. In this case, the transmitting mechanism 5 and the movablesection 6 which constitute a load machine indicate a ball screw and atable respectively, and the non-movable section 7 indicates a base.Moreover, 8 denotes an operation command signal, 9 denotes a rotationdetector signal, and 10 denotes a control signal. Furthermore, 17denotes a signal generator and 18 denotes an FFT analyzer, and both ofthem grasp the frequency characteristic of the load machine and are usedfor devices required for the servo regulation of the control apparatus.

In such an electric motor control apparatus, first of all, the signalgenerator 17 outputs the operation command signal 8 and the operationcommand signal 8 is then sent to the servo control device 2. Next, theoperation command signal 8 input to the servo control device 2 is sentas the control signal 10 to the electric motor 4, and operates themovable section 6 through the transmitting mechanism 5 by the rotatingforce of the electric motor 4. Thereafter, the rotation detector 3 sendsthe rotation detector signal 9 of the electric motor 4 to the FFTanalyzer 18 through the servo control device 2. Subsequently, the FFTanalyzer 18 carries out a fast Fourier calculation by using theoperation command signal 8 received from the signal generator 17 and therotation detector signal 9 received from the servo control device 2 andthen calculates a frequency characteristic, and decides thecharacteristic of the load machine from the result of the calculation.

In the conventional art, however, the expensive FFT analyzer 18 isrequired for measuring the frequency characteristic of the load machine.Therefore, there is a problem in that the cost of equipment isincreased. In order to decide the frequency characteristic measured bythe FFT analyzer 18, moreover, an operator requires advanced expertiseand experiences for reading a resonance frequency, an anti-resonancefrequency and an attenuation. For this reason, there is a problem inthat time and labor are taken.

When the servo regulation of the electric motor control apparatus is tobe carried out, therefore, there has been required an apparatus capableof automatically reading an anti-resonance frequency, a resonancefrequency and an attenuation from a frequency characteristic obtained byan actual measurement and modeling the characteristic of a machine whichcan be utilized for the simulation and the servo regulation of thecontrol apparatus.

The invention has been made in order to solve the problems and has anobject to provide a machine model estimating device of an electric motorcontrol apparatus which can estimate a machine model to be easilyutilized for a simulation and a servo regulation by automaticallyreading an anti-resonance frequency, a resonance frequency and anattenuation from a frequency characteristic measured value without usingan expensive measuring apparatus even if an operator has neitheradvanced expertise nor experiences, and is inexpensive.

DISCLOSURE OF THE INVENTION

In order to solve the problems, a first aspect of the invention isdirected to a machine model estimating device of an electric motorcontrol apparatus comprising an electric motor for driving a loadmachine, a rotation detector for detecting a rotating angle of theelectric motor, and a servo control device for controlling the electricmotor, comprising a calculating device for outputting an operationcommand signal for operating the electric motor to the servo controldevice, and frequency characteristic equations for a rigid body modeland an N-inertia model (N is an integer which is equal to or greaterthan 2) which are previously input to the calculating device, whereinthe calculating device includes a frequency characteristic measuringsection for measuring a frequency characteristic from the operationcommand signal and a signal of the rotation detector input from theservo control device to the calculating device, a frequencycharacteristic peak detecting section for automatically calculatingprotruded shapes to be a resonance frequency and an anti-resonancefrequency from a shape of the frequency characteristic measured by thefrequency characteristic measuring section, an attenuation estimationvalue analyzing section for estimating an attenuation from the resonancefrequency and the anti-resonance frequency which are detected by thefrequency characteristic peak detecting section, a frequencycharacteristic error calculating section for calculating errors of thefrequency characteristics calculated in the frequency characteristicequation for the N-inertia model and the frequency characteristicequation for the rigid body model from the frequency characteristicobtained by the measurement respectively, and a machine model decidingsection for comparing a minimum error of a calculated value of thefrequency characteristic of the N-inertia model which is obtained in thefrequency characteristic error calculating section and a measured valuewith a minimum error of a calculated value of the frequencycharacteristic of the rigid body model and a measured value anddeciding, as an actual model, either of the models which has a smallererror.

Moreover, a second aspect of the invention is directed to the machinemodel estimating device of an electric motor control apparatus accordingto the first aspect of the invention, wherein the frequencycharacteristic error calculating section carries out curve fitting ofthe frequency characteristic obtained from the operation command signaland the signal of the rotation detector to the frequency characteristicequation, thereby calculating an error of the calculated value of thefrequency characteristic and the measured value.

Furthermore, a third aspect of the invention is directed to a machinemodel estimating device of an electric motor control apparatuscomprising an electric motor for driving a load machine, a vibrationdetector for detecting a vibration of the load machine, and a servocontrol device for controlling the electric motor, comprising acalculating device for outputting an operation command signal foroperating the electric motor to the servo control device, and frequencycharacteristic equations of a rigid body model and an N-inertia model (Nis an integer which is equal to or greater than 2) which are previouslyinput to the calculating device, wherein the calculating device includesa frequency characteristic measuring section for measuring a frequencycharacteristic from the operation command signal and a signal of thevibration detector input from the servo control device to thecalculating device, a frequency characteristic peak detecting sectionfor automatically calculating protruded shapes to be a resonancefrequency and an anti-resonance frequency from a shape of the frequencycharacteristic measured by the frequency characteristic measuringsection, an attenuation estimation value analyzing section forestimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section, a frequency characteristic errorcalculating section for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation forthe N-inertia model and the frequency characteristic equation for therigid body model from the frequency characteristic obtained by themeasurement respectively, and a machine model deciding section forcomparing a minimum error of a calculated value of the frequencycharacteristic of the N-inertia model which is obtained in the frequencycharacteristic error calculating section and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.

In addition, a fourth aspect of the invention is directed to the machinemodel estimating device of an electric motor control apparatus accordingto the third aspect of the invention, wherein the frequencycharacteristic error calculating section carries out curve fitting ofthe frequency characteristic obtained from the operation command signaland the signal of the vibration detector to the frequency characteristicequation, thereby calculating an error of the calculated value of thefrequency characteristic and the measured value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto a first embodiment of the invention.

FIG. 2 is a block diagram showing the structure of a calculating deviceaccording to the first embodiment.

FIG. 3 is a flow chart related to the measurement of a frequencycharacteristic in a procedure for the calculating operation of thecalculating device according to the first embodiment.

FIG. 4 is a flow chart related to an operation for deciding a machinemodel based on the frequency characteristic value thus measured in theprocedure for the calculating operation of the calculating deviceaccording to the first embodiment.

FIG. 5 is a view schematically showing a rigid body model.

FIG. 6 is a view schematically showing a 2-inertia model.

FIG. 7 is a chart showing an example of the frequency characteristic ofthe rigid body model according to the first embodiment.

FIG. 8 is a chart showing an example of the frequency characteristic ofthe 2-inertia model according to the first embodiment.

FIG. 9 is a chart showing an example of a curve fitting result of arigid body model type according to the first embodiment.

FIG. 10 is a chart showing an example of a curve fitting result of a2-inertia model type according to the first embodiment.

FIG. 11 is a chart showing an example of the curve fitting result of the2-inertia model type having a great error according to the firstembodiment.

FIG. 12 is a chart showing an example of an unfitness to the curvefitness of the rigid body model type according to the first embodiment.

FIG. 13 is a chart showing an example of an unfitness to the curvefitness of the 2-inertia model type according to the first embodiment.

FIG. 14 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto a second embodiment of the invention.

FIG. 15 is a view showing the whole structure of an electric motorcontrol apparatus according to the conventional art.

BEST MODE FOR CARRYING OUT THE INVENTION

Embodiments of the invention will be described below with reference tothe drawings.

[First Embodiment]

FIG. 1 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto a first embodiment of the invention, and FIG. 2 is a block diagramshowing the structure of a calculating device. The same components ofthe invention as those in the conventional art have the same referencenumerals and description thereof will be omitted, and furthermore, onlydifferences will be described.

In the drawing, 1 denotes a calculating device, 1A denotes a frequencycharacteristic measuring section, 1B denotes a frequency characteristicpeak detecting section, 1C denotes an attenuation estimation valueanalyzing section, 1D denotes a frequency characteristic errorcalculating section, 1E denotes a machine model deciding section, 19denotes an input device, 20 denotes a frequency characteristic equation,and 21 denotes an output device.

The invention is different from the conventional art as follows.

More specifically, there are provided the calculating device 1 foroutputting, to a servo control device 2, an operation command signal 8to operate an electric motor 4, and the frequency characteristicequations 20 for a rigid body model and a 2-inertia model which arepreviously input to the calculating device 1.

Moreover, the calculating device 1 includes the frequency characteristicmeasuring section 1A for measuring the frequency characteristic of aload machine from the operation command signal 8 and a signal 9 of arotation detector 3 which is input from the servo control device 2 tothe calculating device 1, the frequency characteristic peak detectingsection 1B for automatically calculating protruded shapes to be aresonance frequency and an anti-resonance frequency from a shape of thefrequency characteristic measured by the frequency characteristicmeasuring section 1A, the attenuation estimation value analyzing section1C for estimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section 1B, the frequency characteristicerror calculating section 1D for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation 20for the 2-inertia model and the frequency characteristic equation 20 forthe rigid body model from the frequency characteristic obtained by themeasurement respectively, and the machine model deciding section 1E forcomparing a minimum error of a calculated value of the frequencycharacteristic of the 2-inertia model which is obtained in the frequencycharacteristic error calculating section 1D and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.

Furthermore, the frequency characteristic error calculating section 1Dcarries out curve fitting of the frequency characteristic obtained fromthe operation command signal 8 and the signal 9 of the rotation detector3 to the frequency characteristic equation 20, thereby calculating anerror of the calculated value of the frequency characteristic and themeasured value.

Next, an operation will be described.

FIG. 3 is a flow chart related to the measurement of a frequencycharacteristic in a procedure for the calculating operation of thecalculating device according to the first embodiment, and FIG. 4 is aflow chart related to an operation for deciding a machine model based onthe frequency characteristic value thus measured in the procedure forthe calculating operation of the calculating device according to thefirst embodiment.

The procedure for the calculating operation of the calculating device 1is divided into steps ST1 to ST5 for measuring a frequencycharacteristic (FIG. 3) and steps ST6 to ST11 for comparing a modelbased on the frequency characteristic equation 20 with a measuredfrequency characteristic to model the characteristic of a machine (FIG.4).

The processings of the steps ST4 and ST5 correspond to the frequencycharacteristic measuring section 1A of the calculating device 1 shown inFIG. 2, and the processings of the steps ST6 and ST7 correspond to thefrequency characteristic peak detecting section 1B. Moreover, theprocessing of the step ST8 corresponds to the attenuation estimationvalue analyzing section 1C, the processings of the steps ST9 and ST10correspond to the frequency characteristic error calculating section 1D,and the processing of the step ST11 corresponds to the machine modeldeciding section 1E.

First of all, the measurement of the frequency characteristic in thesteps ST1 to ST5 will be described with reference to FIG. 3.

At the step ST1, first of all, the calculating device 1 creates theoperation command signal 8.

At the step ST2, next, the operation command signal 8 output from thecalculating device 1 is transferred to the servo control device 2.Consequently, an equivalent control signal 10 to the operation commandsignal 8 is sent to the electric motor 4 and the electric motor 4 isoperated so that a movable section 6 is operated through a transmittingmechanism 5 and generates a vibration.

At the step ST3, then, the rotation detector 3 detects the rotationdetector signal 9 in the rotating operation of the electric motor 4 andtransfers the rotation detector signal 9 to the calculating device 1 viathe servo control device 2.

At the step ST4, thereafter, ah FFT calculation is carried out over theoperation command signal 8 and the rotation detector signal 9 by thecalculating device 1, thereby performing a frequency analysis, forexample.

At the step ST5, a frequency characteristic is calculated from theoperation command signal 8 and the rotation detector signal 9 which aresubjected to the frequency analysis in the calculating device 1. Bythese processings, the frequency characteristic is completely measured.

Next, description will be given to the modeling of the machinecharacteristic at the steps ST6 to ST11.

Referring to the modeling of the machine characteristic, first of all, adivision into the rigid body model and the 2-inertia model can becarried out as shown in FIGS. 5 and 6, respectively.

FIG. 5 is a view schematically showing the rigid body model. Morespecifically, the electric motor 4, the transmitting mechanism 5 and themovable section 6 shown in FIG. 1 are caused to approximate to a simplerigid body load 11 (J1+J2). Moreover, FIG. 6 is a view schematicallyshowing the 2-inertia model. More specifically, the electric motor 4,the transmitting mechanism 5 and the movable section 6 shown in FIG. 1are caused to approximate to the 2-inertia model by two boxes having anelectric motor side load 12 (J1) and a load side load 13 (J2), and aspring 14 a (K: spring constant) and an attenuation 14 b (D: attenuationconstant) connecting the two boxes 12 and 13. The rigid body load 11 isequivalent to (J1+J2) to be the sum of the electric motor side load 12and the load side load 13.

In the rigid body model shown in FIG. 5, a model equation for afrequency characteristic Hr from the operation command signal 8 to therotation detector signal 9 is obtained as shown in Equation (1).$\begin{matrix}{H_{r} = \frac{1}{\left( {J_{1} + J_{2}} \right) \cdot s}} & {{Equation}\quad(1)}\end{matrix}$

In the 2-inertia model shown in FIG. 6, moreover, a model equation for afrequency characteristic H_(f) from the operation command signal 8 tothe rotation detector signal 9 is obtained as shown in Equation (2).$\begin{matrix}{H_{f} = {\frac{1}{J_{1} \cdot s} \cdot \frac{{S2} + {\frac{D}{J2} \cdot s} + \frac{K}{J2}}{s^{2} + {\left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right) \cdot D \cdot s} + {\left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right) \cdot K}}}} & {{Equation}\quad(2)}\end{matrix}$

In order to approximate to the rigid body model, it is sufficient thatthe sum (J1+J2) of the rigid body load 11 or the electric motor sideload 12 and the load side load 13 is clear.

In order to approximate to the 2-inertia model, moreover, it issufficient that the electric motor side load 12 (J1) and the load sideload 13 (J2), and the spring constant K and the attenuation constant Dare clear.

FIG. 7 is a chart showing an example of the frequency characteristic ofthe rigid body model according to the first embodiment and FIG. 8 is achart showing an example of the frequency characteristic of the2-inertia model according to the first embodiment, and both of them arecharts showing a calculation using model equations.

The Equation (1) for the rigid body model represents a gaincharacteristic which is smooth, rightward and downward as shown in FIG.7, while the Equation (2) for the 2-inertia model represents a gaincharacteristic having a protruded shape, that is, a mountain and avalley as shown in FIG. 8. The valley and mountain sides shown in FIG. 8are referred to as an anti-resonance and a resonance respectively, andan anti-resonance frequency F_(L) and a resonance frequency F_(H) canapproximate to Equations (3) and (4) from the Equation (2) respectively.$\begin{matrix}{f_{L} = {\frac{1}{2\pi} \cdot \sqrt{\frac{K}{J_{2}}}}} & {{Equation}\quad(3)}\end{matrix}$ $\begin{matrix}{f_{H} = {\frac{1}{2\pi} \cdot \sqrt{K \cdot \left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right)}}} & {{Equation}\quad(4)}\end{matrix}$

In this case, the rightward and downward inclination of a low frequencyregion in FIG. 8 can approximate to the Equation (1).

For this reason, the sum (J1+J2) of the anti-resonance frequency FL, theresonance frequency F_(H), the electric motor side load 12 and the loadside load 13 is clear and an approximation to the 2-inertia model can becarried out.

Moreover, the sum (J1+J2) of the electric motor side load 12 and theload side load 13 can be calculated by the dimension and physicalcharacteristic of the load machine. If (J1+J2) is previously calculated,furthermore, it can be input in the input device 19 connected to thecalculating device 1 (described in INERTIA IDENTIFYING METHOD ANDAUTOTUNING: VOL. 62, NO. 4, Technical Report “YASKAWA ELECTRICCORPORATION”, for example).

From the foregoing, it is possible to compare the measured frequencycharacteristic with the Equations (1) and (2), thereby deciding whethereither the rigid body model or the 2-inertia model is suitable.

FIG. 9 is a chart showing an example of the curve fitting result of therigid body model type according to the first embodiment and FIG. 10 is achart showing an example of the curve fitting result of the 2-inertiamodel type according to the first embodiment. In the drawing, a solidline indicates a measured value and a broken line indicates a curvefitting result.

Since a frequency characteristic changed smoothly from a left and upperportion toward a right and lower portion represents a measurement resultin FIG. 9, curve fitting can be carried out with a small error from thegraph of the Equation (1). On the other hand, since a frequencycharacteristic having a plurality of valleys and mountains represents ameasurement result in FIG. 10, the curve fitting can be carried out witha small error from the graph of the Equation (2).

A processing of modeling a machine characteristic will be executed belowby using the steps ST6 to ST11 in the flow chart of FIG. 3.

At the step ST6, first of all, a peak on a mountain side and a peak on avalley side are calculated from the frequency characteristic measured atthe step ST5. For a method of calculating the peak, it is preferable touse a complex spectrum interpolating method and a smoothingdifferentiation method which are well-known.

If the peak on the mountain side and the peak on the valley side cannotbe detected at the step ST7, next, the model can be decided to be arigid body and the parameter of the rigid body can be determined by onlya load inertia to be the sum (J1+J2) of the electric motor side load 12and the load side load 13. If the peak can be detected at the step ST7,the processing proceeds to the step ST8 in which an attenuation can beestimated based on a well-known attenuation estimating method by usingthe frequency of the peak thus detected.

If the sum (J1+J2) of the electric motor side load 12 and the load sideload 13 is undecided, moreover, the load inertia (J1+J2) may becalculated, by a least square method using the Equation (1), from thelow frequency region of the measured frequency characteristic which islower than the peak on the valley side.

If a plurality of peaks can be detected, furthermore, a plurality ofcombinations to be a pair of the peak on the mountain side and the peakon the valley side is set and created.

At the step ST9, then, the combination to be a peak pair which is settemporarily is subjected to curve fitting by the frequencycharacteristic equation 20 for the 2-inertia model input previously tothe calculating device 1, that is, the Equation (2), thereby calculatingan error of the Equation (2) and the measured value.

Errors of results obtained by the curve fitting and the measuredfrequency characteristics are calculated by using the combinations ofthe peaks, respectively.

Since the load inertia (J1+J2), a pair of the peak on the mountain sideand the peak on the valley side, that is, a resonance and ananti-resonance frequency, and an attenuation are clear, it is possibleto carry out the curve fitting by substituting each value for theEquation (2) to be one of the frequency characteristic equations 20input to the calculating device 1.

Any of the combinations which has a small error from the result obtainedby the curve fitting makes a set of the peak on the mountain side andthe peak on the valley side which is optimum for the 2-inertia model.

FIG. 11 is a chart showing an example of the curve fitting result of the2-inertia model type having a great error according to the firstembodiment. In the drawing, a solid line indicates a measured value anda broken line indicates a curve fitting result.

For example, a set of the resonance and the anti-resonance shown in FIG.10 has a small error, while a set of the resonance and theanti-resonance shown in FIG. 11 has a great error. Therefore, it isclear that the set of FIG. 10 is optimum for the resonance and theanti-resonance in the 2-inertia model.

At the step ST10, also in the case in which the peak is detected, anerror of the result obtained by the curve fitting to the rigid bodymodel and the measured frequency characteristic is calculated by theEquation (1) to be one of the frequency characteristic equations 20input to the calculating device 1.

At the step ST11, next, the minimum error of the 2-inertia model iscompared with the error of the rigid body model. If the error of therigid body model is small, a decision of the rigid body model can bemade. If the error of the 2-inertia model is small, a decision of the2-inertia model can be made. In other words, it is possible to comparethe error of the Equation (2) and the measured frequency characteristicwith the error of the Equation (1) and the measured frequencycharacteristic, thereby deciding which error is smaller and which modelis optimum in the modeling for the Equation (1) and the Equation (2).

FIG. 12 is a chart showing an example of an unfitness to the curvefitting of the rigid body model type according to the first embodimentand FIG. 13 is a chart showing an example of an unfitness to the curvefitting of the 2-inertia model type according to the first embodiment.In the drawing, a solid line indicates a measured value and a brokenline indicates a curve fitting result.

While the measured value of the frequency characteristic in FIG. 12 hasa plurality of valleys and mountains, for example, the curve fitting iscarried out in the Equation (1) for the rigid body model so that anerror is great. As shown in FIG. 10, however, the error is small in theEquation (2) for the 2-inertia model. Therefore, a decision of the2-inertia model can be made.

While the frequency characteristic changed smoothly from a left andupper portion toward a right and lower portion represents a measurementresult, moreover, the curve fitting is carried out in the Equation (2)for the 2-inertia model so that the error is great. As shown in FIG. 9,however, the error is small in the Equation (1) for the rigid bodymodel. Therefore, a decision of the rigid body model can be made.

When the decision of the model is completed, the result can be output tothe output device 21 connected to the calculating device 1 and can beutilized for a simulation and the regulation of the electric motorcontrol apparatus.

Accordingly, the first embodiment is characterized by the electric motorcontrol apparatus comprising the electric motor 4 for driving a loadmachine, the rotation detector 3 for detecting the rotating angle of theelectric motor 4, and the servo control device 2 for controlling theelectric motor 4, comprising the calculating device 1 for outputting theoperation command signal 8 for operating the electric motor 4 to theservo control device 2, and the frequency characteristic equations 20 ofthe rigid body model and the 2-inertia model which are previously inputto the calculating device 1. Moreover, the calculating device-1 includesthe frequency characteristic measuring section 1A for measuring thefrequency characteristic of the load machine from the operation commandsignal 8 and the signal 9 of the rotation detector 3 input from theservo control device 2 to the calculating device 1, the frequencycharacteristic peak detecting section 1B for automatically calculatingprotruded shapes to be a resonance frequency and an anti-resonancefrequency from a shape of the frequency characteristic measured by thefrequency characteristic measuring section 1A, the attenuationestimation value analyzing section 1C for estimating an attenuation fromthe resonance frequency and the anti-resonance frequency which aredetected by the frequency characteristic peak detecting section 1B, thefrequency characteristic error calculating section 1D for calculatingerrors of the frequency characteristics calculated in the frequencycharacteristic equation 20 for the 2-inertia model and the frequencycharacteristic equation 20 for the rigid body model from the frequencycharacteristic obtained by the measurement respectively, and the machinemodel deciding section 1E for comparing a minimum error of a calculatedvalue of the frequency characteristic of the 2-inertia model which isobtained in the frequency characteristic error calculating section 1Dand a measured value with a minimum error of a calculated value of thefrequency characteristic of the rigid body model and a measured valueand deciding, as an actual model, either of the models which has asmaller error. Furthermore, the frequency characteristic errorcalculating section 1D carries out curve fitting of the frequencycharacteristic obtained from the operation command signal 8 and thesignal 9 of the rotation detector 3 to the frequency characteristicequation 20, thereby calculating an error of the calculated value of thefrequency characteristic and the measured value. Consequently, it ispossible to provide a machine model estimating device of an electricmotor control apparatus which can faithfully estimate a machine model tobe easily utilized for a simulation and a servo regulation byautomatically reading an anti-resonance frequency, a resonance frequencyand an attenuation from a frequency characteristic measured valuewithout using an expensive measuring apparatus even if an operator hasneither advanced expertise nor experiences, and furthermore, isinexpensive.

[Second Embodiment]

A second embodiment of the invention will be described with reference tothe drawings.

FIG. 14 is a view showing the whole structure of an electric motorcontrol apparatus comprising a machine model estimating device accordingto the second embodiment of the invention.

In the drawing, 15 denotes a vibration detector for detecting theoperation state of a load machine as a vibration displacement or avibration acceleration, and 16 denotes a vibration detector signal ofthe load machine.

The second embodiment uses the vibration detector 15 in the load machinein place of the rotation detector described in the first embodiment, andcan be executed in the same manner as the first embodiment.

A frequency characteristic Hr from an operation command signal 8 of arigid body model to the detector 15 is equal to that of the Equation(1). Moreover, a frequency characteristic H′_(F) from the operationcommand signal 8 of a 2-inertia model to the vibration detector 15 of aload side load 13 is obtained from Equation (5). $\begin{matrix}{H_{f}^{7} = {\frac{1}{J_{1} \cdot J_{2} \cdot s} \cdot \frac{{D \cdot s} + K}{s^{2} + {\left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right) \cdot D \cdot s} + {K \cdot \left( {\frac{1}{J_{1}} + \frac{1}{J_{2}}} \right) \cdot K}}}} & {{Equation}\quad(5)}\end{matrix}$

Thus, the second embodiment is executed by using the vibration detector15 in the load machine in place of the rotation detector according tothe first embodiment. In the same manner as in the first embodiment,consequently, a peak on the mountain side of the measured frequencycharacteristic is estimated, an attenuation is estimated, a load inertiais estimated, a resonance is temporarily determined, and the resonanceis compared with the frequency characteristic of the model with a changeand a resonance having a small error is obtained, and furthermore, thefrequency characteristics of the rigid body model and the 2-inertiamodel are compared with each other, the model having a smaller error isdistinguished, and the measured frequency characteristic is subjected tocurve fitting. Thus, modeling can be faithfully executed.

While the two machine models, that is, the rigid body model and the2-inertia model are used in the embodiment, another model such as a3-inertia model may be used or the type of the model to be distinguishedmay be increased.

While only the model and the error of the measured frequencycharacteristic are set to be evaluation criteria in the embodiment,moreover, the value of the gain of a resonance frequency, the widths ofthe gains of an anti-resonance frequency and the resonance frequency,and a frequency may be added to the evaluation criteria.

INDUSTRIAL APPLICABILITY

As described above, the machine model estimating device of the electricmotor control apparatus according to the invention is useful for theservo regulation of an electric motor control apparatus to be used in asemiconductor manufacturing apparatus, a positioning apparatus such as amachine tool or an industrial robot, for example.

1. A machine model estimating device of an electric motor controlapparatus comprising an electric motor for driving a load machine, arotation detector for detecting a rotating angle of the electric motor,and a servo control device for controlling the electric motor,comprising a calculating device for outputting an operation commandsignal for operating the electric motor to the servo control device, andfrequency characteristic equations for a rigid body model and anN-inertia model, N being an integer which is equal to or greater than 2which are previously input to the calculating device, wherein thecalculating device includes a frequency characteristic measuring sectionfor measuring a frequency characteristic from the operation commandsignal and a signal of the rotation detector input from the servocontrol device to the calculating device, a frequency characteristicpeak detecting section for automatically calculating protruded shapes tobe a resonance frequency and an anti-resonance frequency from a shape ofthe frequency characteristic measured by the frequency characteristicmeasuring section, an attenuation estimation value analyzing section forestimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section, a frequency characteristic errorcalculating section for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation forthe N-inertia model and the frequency characteristic equation for therigid body model from the frequency characteristic obtained by themeasurement respectively, and a machine model deciding section forcomparing a minimum error of a calculated value of the frequencycharacteristic of the N-inertia model which is obtained in the frequencycharacteristic error calculating section and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.
 2. The machinemodel estimating device of an electric motor control apparatus accordingto claim 1, wherein the frequency characteristic error calculatingsection carries out curve fitting of the frequency characteristicobtained from the operation command signal and the signal of therotation detector to the frequency characteristic equation, therebycalculating an error of the calculated value of the frequencycharacteristic and the measured value.
 3. A machine model estimatingdevice of an electric motor control apparatus comprising an electricmotor for driving a load machine, a vibration detector for detecting avibration of the load machine, and a servo control device forcontrolling the electric motor, comprising a calculating device foroutputting an operation command signal for operating the electric motorto the servo control device, and frequency characteristic equations of arigid body model and an N-inertia model N being an integer which isequal to or greater than 2 which are previously input to the calculatingdevice, wherein the calculating device includes a frequencycharacteristic measuring section for measuring a frequencycharacteristic from the operation command signal and a signal of thevibration detector input from the servo control device to thecalculating device, a frequency characteristic peak detecting sectionfor automatically calculating protruded shapes to be a resonancefrequency and an anti-resonance frequency from a shape of the frequencycharacteristic measured by the frequency characteristic measuringsection, an attenuation estimation value analyzing section forestimating an attenuation from the resonance frequency and theanti-resonance frequency which are detected by the frequencycharacteristic peak detecting section, a frequency characteristic errorcalculating section for calculating errors of the frequencycharacteristics calculated in the frequency characteristic equation forthe N-inertia model and the frequency characteristic equation for therigid body model from the frequency characteristic obtained by themeasurement respectively, and a machine model deciding section forcomparing a minimum error of a calculated value of the frequencycharacteristic of the N-inertia model which is obtained in the frequencycharacteristic error calculating section and a measured value with aminimum error of a calculated value of the frequency characteristic ofthe rigid body model and a measured value and deciding, as an actualmodel, either of the models which has a smaller error.
 4. The machinemodel estimating device of an electric motor control apparatus accordingto claim 3, wherein the frequency characteristic error calculatingsection carries out curve fitting of the frequency characteristicobtained from the operation command signal and the signal of thevibration detector to the frequency characteristic equation, therebycalculating an error of the calculated value of the frequencycharacteristic and the measured value.